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Matthew Quick is a geographic information scientist and urban planner who focuses on the development of quantitative methods for spatial and spatiotemporal data and the application of these methods to issues in urban planning, criminology, urban geography, and health. His current research areas are in the development of spatiotemporal models for multiple outcomes, policy evaluation, neighborhood change, and cluster identification. His research aims to inform and evaluate place-based policies focused on community safety, public health, and socio-spatial inequality. He earned his doctorate in 2019 from the School of Planning at the University of Waterloo, Canada.
Spatial and spatiotemporal data analysis, geographic information science, urban planning and public policy, urban geography, crime and place, spatial epidemiology, Bayesian hierarchical modeling
Quick, M. (2019). Multiscale spatiotemporal crime patterns: A Bayesian cross-classified multilevel modeling approach. Journal of Geographical Systems, 21(3), 339-365.
Quick, M., Law, J., and Li, G. (2019). Time-varying relationships between land use and crime: A spatio-temporal analysis of small-area seasonal property crime trends. Environment and Planning B: Urban Analytics and City Science, 46(6), 1018-1035.
Moos, M., Filion, P., Quick, M., and Walter-Joseph, R. (2019). Generationed cities: Exploring the residential geographies of young adults in North American metropolitan regions. Cities, 93, 224-237.
Quick, M., Li, G., and Law, J. (2019). Spatiotemporal modelling of correlated small-area outcomes: Analyzing the shared and type-specific patterns of crime and disorder. Geographical Analysis, 51(2), 221-248.
Quick, M., Li, G., and Brunton-Smith, I. (2018). Crime-general and crime-specific spatial patterns: A multivariate spatial analysis of four crime types at the small-area scale. Journal of Criminal Justice, 58, 22-32.
Quick, M., Law, J., and Luan, H. (2017). The influence of on-premise and off-premise alcohol outlets on reported violent crime in the Region of Waterloo, Ontario: Applying Bayesian spatial modeling to inform land use planning and policy. Applied Spatial Analysis and Policy, 10(3), 435-454.